- Introduction
- Why Neuro-Symbolic AI
- Neuro-symbolic AI strives to blend the strengths of both domains
- Neuro Symbolic AI with a mission
- Important Terms Applicable to Neuro-Symbolic AI
- Important constituents of Neuro Symbolic AI
- The diagram below illustrates the utilization of AI techniques in imaging classification and segmentation for biomedical purposes.
- Neuro-Symbolic: Progressive & Promising
- Benefits of Neuro-Symbolic AI
- Future Outlook of AI in the Era of Neuro-Symbolic AI
- RAAPID’s clinical solution engineering is based on Neuro-symbolic AI technology
- Conclusion
Introduction
In the ever-evolving realm of Artificial Intelligence (AI), a novel strategy is gaining traction:
Neuro-Symbolic AI
This progressive method seeks to unify two historically separate domains: neural networks, renowned for data-driven learning, and symbolic AI, known for its prowess in logical reasoning.
By integrating these capabilities, Neuro-Symbolic AI has the potential to unleash unprecedented levels of comprehension, proficiency, and adaptability within AI frameworks.
This article delves into the intricacies of Neuro-Symbolic AI, examining its genesis, possibilities, and repercussions for AI’s trajectory. We’ll discuss how RAAPID leverages Neuro-Symbolic AI technology to boost risk adjustment solutions.
Genesis of Neuro-Symbolic AI
Neuro-symbolic AI emerges from continuous efforts to emulate human intelligence in machines. Conventional AI models usually align with either neural networks, adept at discerning patterns from data, or symbolic AI, reliant on predefined knowledge for decision-making.
The diagram below demonstrates the process of conducting a COVID-19 symptom test using two approaches: an AI-based method represented by the left-side flow and a conventional (non-AI) approach depicted by the right-side flow.
Neural Networks
These networks draw inspiration from the human brain, comprising layers of interconnected nodes, commonly called “neurons,” capable of learning from data. They exhibit notable proficiency in processing unstructured data such as images, sounds, and text, forming the foundation of deep learning. Renowned for their adeptness in pattern recognition, neural networks can forecast or categorize based on historical instances. An everyday illustration of neural networks in action lies in image recognition. Take, for instance, any of the social media’s utilization of neural networks for its automated tagging functionality. As you upload a photo, the neural network model, having undergone extensive training with ample data, discerns and distinguishes faces. Subsequently, it can anticipate and propose tags grounded on the identified faces within your image.
Symbolic Reasoning
In this method, symbols denote concepts, and logic analyzes them—a process akin to how humans utilize language and structured cognition to comprehend the environment. Symbolic AI excels in activities demanding comprehension of rules, logic, or structured information, such as puzzle-solving or navigating intricate problems through reasoning.
Yet another instance of symbolic AI manifests in rule-based systems, such as those that solve queries. Such as the NSQA (Neuro-Symbolic Question Answering) Model. Here, the AI relies on pre-established rules and logic.
For instance, let us pose NSQA the question, “Was Elvis Presley born in England?” (See the diagram below).
So, to verify Elvis Presley’s birthplace, specifically whether he was born in England refer the above diagram , the system initially converts the question into a generic logical form by translating it into an Abstract Meaning Representation (AMR). Each AMR encapsulates the meaning of the question using terminology independent of the knowledge graph, a crucial feature enabling the technology’s application across various tasks and knowledge bases.
The above Diagram shows the AMR to align with the terms in the knowledge graph through entity-linking and relation-linking modules, after which it undergoes transformation into a logical representation. This logical representation is then processed by the Logical Neural Network (LNN), which conducts necessary reasoning, such as type-based and geographic reasoning to finally provide answers to the posed question
Why Neuro-Symbolic AI
Upon delving into human cognition and reasoning, it’s evident that symbols play a pivotal role in concept understanding and decision-making, thereby enhancing intelligence. Researchers endeavored to emulate this symbol-centric aspect in robots to align their operations closely with human capabilities. This entailed incorporating explicit human knowledge and behavioral guidelines into computer programs, forming the basis of rule-based symbolic AI. However, this approach heightened system costs and diminished accuracy with the addition of more rules.
Imagine a traditional symbolic AI system designed to diagnose diseases based on symptoms. This system relies on a set of rules written by medical experts. For instance:
- If a patient has a fever and a sore throat, they might have a cold.
- If a patient has chest pain and shortness of breath, they might have a heart condition.
However, traditional symbolic AI struggles when presented with uncertain or ambiguous information. For example, if a patient has a mix of symptoms that don’t fit neatly into any predefined rule, the system might struggle to make an accurate diagnosis. Additionally, if new symptoms or diseases emerge that aren’t explicitly covered by the rules, the system will be unable to adapt without manual intervention to update its rule set.
Researchers explored a data-centric approach to tackle these challenges, leading to the growing popularity of neural networks. Unlike symbolic AI, which demands continuous information input, neural networks can autonomously train themselves with substantial datasets. However, despite their apparent efficiency, as previously mentioned, there is a demand for improved systems due to the challenges associated with interpreting the model and the extensive data required for ongoing learning.
Neuro-symbolic AI strives to blend the strengths of both domains:
Domain 1– The pattern recognition and learning capacity inherent in neural networks.
Domain2– The structured reasoning and interpretive capabilities characteristic of symbolic AI.
The diagram below exemplifies Neuro-Symbolic AI that includes the above 2 integrated domains forming a single Hybrid AI.
The above diagram shows the neural components having the capability to identify specific aspects, such as components of the COVID-19 virus, while the symbolic elements can depict their logical connections. Collectively, these components can elucidate the mechanisms and underlying reasons behind the actions of COVID-19.
We perceive Neuro-symbolic AI as a route to attain artificial general intelligence. Through enhancing and merging the advantages of statistical AI, such as machine learning, with the prowess of human-like symbolic knowledge and reasoning, our goal is to spark a revolution in AI, rather than a mere evolution.
By integrating these methodologies, neuro-symbolic AI aims to develop systems with the dual ability to learn from data and engage in reasoning akin to humans. Such advancements could result in AI systems that are more robust and adaptable, capable of addressing intricate tasks currently reliant on human intelligence and achieving this with a level of transparency and explainability beyond what neural networks alone can offer.
Neuro Symbolic AI with a mission
Neuro-symbolic AI endeavors to forge a fundamentally novel AI approach to bridge the existing disparities between the current state-of-the-art and the core objectives of AI. Its primary goal is to achieve a harmonious equilibrium between the benefits of statistical AI (machine learning) and the prowess of symbolic or classical AI (knowledge and reasoning). Instead of incremental progress, it aspires to revolutionize the field by establishing entirely new paradigms rather than superficially synthesizing existing ones.
The main objectives of Neuro Symbolic AI are to show that it is capable of
- Addressing even more challenging problems.
- Acquiring knowledge with significantly reduced data requirements, eventually spanning across various tasks rather than being limited to one specific task.
- Generate decisions and behaviors that are intuitively comprehensible and within your realm of control.
Important Terms Applicable to Neuro-Symbolic AI
Below are several fundamental terminologies utilized in neuro-symbolic AI.
- Explanation: Denotes the capacity of a neuro-symbolic AI system to offer explanations that are understandable to humans for its predictions and decisions.
- Neural-Symbolic Integration: Denotes neural and symbolic reasoning fusion within a blended architecture.
- Knowledge Graph: Describes a knowledge representation structured as a graph, where nodes symbolize entities and edges symbolize relationships connecting these entities
- Symbolic Representation: involves utilizing symbols, such as logic, ontologies, and knowledge graphs, to depict knowledge and conduct reasoning activities.
- Blended Architecture: Denotes the fusion of neural networks and symbolic reasoning components within a neuro-symbolic AI framework.
- Ontology: Describes a structured depiction of a collection of concepts and their relationships within a particular domain.
- Reasoning: Describes the capability of a neuro-symbolic AI system to engage in logical inference, theorem proving, and planning using symbolic representations.
- Perception: Refers to the capability of a neuro-symbolic AI system to analyze and comprehend sensory input, including images, speech, and text.
Important constituents of Neuro Symbolic AI
The primary constituents of a neuro-symbolic AI system encompass the following.
- Neural Network: A module tasked with executing perceptual functions employing deep learning algorithms, including tasks like image recognition and natural language processing.
- Symbolic Reasoning Engine: A module responsible for conducting logical inference, theorem validation, and planning through symbolic representations such as logic and knowledge graphs.
- Integration Layer: This module amalgamates the neural network and symbolic reasoning engine to construct a hybrid architecture. It facilitates the translation between symbolic and neural representations and communication between the two modules.
- Knowledge Base: A module designated for storing structured knowledge, encompassing ontologies, knowledge graphs, and relational databases.
- Explanation Generator: A module dedicated to producing human-comprehensible explanations for the predictions and decisions formulated by the neuro-symbolic AI system.
- User Interface: A module enabling human users to interact with the neuro-symbolic AI system, facilitating actions like input provision and output reception.
- These modules collaborate harmoniously to shape a neuro-symbolic AI system adept at performing diverse tasks, leveraging the capabilities of both neural networks and symbolic reasoning.
The diagram below illustrates the utilization of AI techniques in imaging classification and segmentation for biomedical purposes.
Neuro-Symbolic: Progressive & Promising
The potential of Neuro-Symbolic AI is extensive. Through the fusion of learning and reasoning capabilities, these systems have the capacity to comprehend and engage with the world in a manner closely resembling human cognition.
For instance, a Neuro-Symbolic AI system might acquire the ability to identify objects within images (a task conventionally handled by neural networks) while also employing symbolic reasoning to draw inferences about those objects (a task traditionally performed by symbolic AI). This integration could pave the way for more sophisticated AI applications, such as robots capable of navigating intricate environments or virtual assistants adept at comprehending and responding to natural language queries in a manner similar to humans. Consider the scenario of a self-driving car. It must identify various objects such as cars, pedestrians, and traffic signs—a task ideally handled by neural networks. However, it also needs to make decisions based on these identifications and in accordance with traffic regulations—a task better suited for symbolic AI.
In this context, a Neuro-Symbolic AI system would employ a neural network to learn object recognition from data, such as images captured by the car’s cameras. Additionally, it would utilize a symbolic system to reason about these recognized objects and make decisions aligned with traffic rules. This amalgamation enables the self-driving car to interact with its surroundings in a manner akin to human cognition, comprehending the context and making reasoned judgments.
Benefits of Neuro-Symbolic AI
Highlighted below are several key benefits of Neuro-Symbolic AI:
Enhanced Generalization: Neuro-symbolic AI exhibits superior generalization capabilities compared to conventional deep learning models. This stems from the fact that symbolic reasoning can encapsulate high-level concepts even with limited data.
Improved Data Efficiency: Neuro-symbolic AI demonstrates increased data efficiency compared to traditional neural networks. The capacity for symbolic reasoning enables these models to generate precise predictions even when confronted with constrained data sets.
Neuro-symbolic models have showcased their ability to surpass current deep learning models in areas like image and video comprehension. Additionally, they’ve exhibited remarkable accuracy while utilizing notably less training data than conventional models.
Integration of Learning and Reasoning: Neuro-symbolic AI furnishes a platform for amalgamating learning, facilitated by the neural component, and reasoning, conducted by the symbolic component. This integration offers the potential for more sophisticated behavior than each component could independently achieve.
Future Outlook of AI in the Era of Neuro-Symbolic AI
In the days to come, as we look into the future, it becomes evident that ‘Neuro-Symbolic AI harbors the potential to propel the AI field forward significantly. This methodology, by bridging the divide between neural networks and symbolic AI, holds the key to unlocking peak levels of capability and adaptability within AI systems.
Recent years have witnessed numerous breakthroughs in Neuro-Symbolic AI. AI research firms view Neuro-symbolic AI as a route towards attaining artificial general intelligence. By enhancing and merging the strengths of statistical AI, such as machine learning, with human-like symbolic knowledge capabilities and reasoning, they aim to spark a revolution in the field of AI.
Although these advancements represent notable strides in emulating human reasoning abilities, existing versions of Neuro-symbolic AI systems remain insufficient for tackling complex and abstract mathematical problems. Nevertheless, the outlook for AI with Neuro-Symbolic AI appears promising as researchers persist in their exploration and innovation within this domain. The potential for Neuro-Symbolic AI to enhance AI capabilities and adaptability is vast, and further breakthroughs are anticipated in the foreseeable future.
RAAPID’s clinical solution engineering is based on Neuro-symbolic AI technology
RAAPID’s retrospective and prospective risk adjustment solution uses a Clinical Knowledge Graph, a dataset that structures diverse clinical data into a comprehensive, interconnected entity.
This dataset is layered over the Neuro-symbolic AI module, which performs in combination with the neural network’s intuitive, power, and symbolic AI reasoning module. This hybrid approach aims to replicate a more human-like understanding and processing of clinical information, addressing the need for abstract reasoning and handling vast, unstructured clinical data sets.
RAAPID’s retrospective and prospective solution is powered by Neuro-symbolic AI to revolutionize chart coding, reviewing, auditing, and clinical decision support. Our Neuro-Symbolic AI solutions are meticulously curated from over 10 million charts, encompassing over 4 million clinical entities and over 50 million relationships.
By augmenting and combining the strengths of AI with this multi-relational human-curated symbolic knowledge, our retrospective and prospective solution ensures a nuanced understanding of clinical data and helps the clinical team achieve accuracy & efficiency during code review & code updates resulting in effective audit compliance and appropriate reimbursement for health plans.
RAAPID’s neuro-symbolic AI is a quantum leap in risk adjustment, where AI can more accurately model human thought processes. This reflects our commitment to evolving with the need for positive risk adjustment outcomes through superior data intelligence.
Conclusion
In the constantly changing landscape of Artificial Intelligence (AI), the emergence of Neuro-Symbolic AI marks a promising advancement. This innovative approach unites neural networks and symbolic reasoning, blending their strengths to achieve unparalleled levels of comprehension and adaptability within AI systems. By delving into the genesis, functionalities, and potential applications of Neuro-Symbolic AI, we uncover its transformative impact on various domains, including risk adjustment in clinical settings.
RAAPID leverages Neuro-Symbolic AI to revolutionize clinical decision-making and risk adjustment processes. By seamlessly integrating a Clinical Knowledge Graph with Neuro-Symbolic AI capabilities, RAAPID ensures a comprehensive understanding of intricate clinical data, facilitating precise risk assessment and decision support. Our solution, meticulously crafted from extensive clinical records, embodies a groundbreaking advancement in healthcare analytics.
With a commitment to innovation and excellence, RAAPID continues to lead the way in transforming the risk adjustment environment. This empowers organizations to make informed decisions and deliver superior patient care, resulting in compliant ROI.
